https://dasya.itu.dk/semester/spring-2021/ Recent content in Spring 2021 on DASYA Hugo -- gohugo.io en-us © DASYA, IT University of Copenhagen Fri, 16 Apr 2021 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/inferenceinkvs/ Fri, 16 Apr 2021 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/inferenceinkvs/ (This project will be carried out in collaboration with Xilinx Research Labs in Dublin) Machine Learning operators are becoming increasingly commonly used in data management systems and, in this project, we will explore the challenges and benefits of integrating inference operators from FINN [1] within a so-called Smart Storage system [2]. Both the inference and data management aspects will be handled by an FPGA in order to provide small energy footprint and guarantee high access bandwidth at the same time (see details below). https://dasya.itu.dk/for-students/proposals/archive/mpc-databases/ Mon, 15 Mar 2021 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/mpc-databases/ (This topic is going to be co-supervised by Bernardo Machado David [http://www.bmdavid.com/]) Database systems managing private data may leak sensitive information when queries are done in the clear, even if the data itself is encrypted. A recent line of research has looked into combining database engines supporting standard SQL queries with techniques for secure Multiparty Computation (MPC), which allow for both queries and data to be handled privately. In these hybrid systems, not only the data is protected but the queries made by legitimate users are kept private even from the database engine itself (and consequently from the systems executing this engine). https://dasya.itu.dk/for-students/projects/archive/clustering-of-high-dimensional-data/ Sat, 20 Feb 2021 00:00:00 +0000 https://dasya.itu.dk/for-students/projects/archive/clustering-of-high-dimensional-data/ Designing evaluation schemes for cluster analysis of high-dimensional data using biased example clusters. https://dasya.itu.dk/for-students/proposals/archive/consensus/ Mon, 14 Dec 2020 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/consensus/ Consensus mechanisms for ensuring consistency are some of the most expensive operations in managing large amounts of data. Often, there is a trade off that involves reducing the coordination overhead at the price of accepting possible data loss or inconsistencies. As the demand for more efficient data centers increases, it is important to provide better ways of ensuring consistency without affecting performance. In recent years, there have been several research proposals around Crash Fault Tolerant (e. https://dasya.itu.dk/for-students/proposals/archive/privacypreserving/ Tue, 08 Dec 2020 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/privacypreserving/ Given a private database that I can access only through specific queries, there is still a lot I can learn on its entries [1]. Differential Privacy (DP) tackles this: letting me learn the (approximate) result of complex queries on a database, but preventing me from learning much about its specific entries. The basic approach of DP often boils down to: “apply a privacy-preserving transformation T on the result of the query F or on the data itself before computing F”. https://dasya.itu.dk/for-students/proposals/archive/automl-forecasting-energy/ Thu, 20 Aug 2020 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/automl-forecasting-energy/ With the recent hunger for being “data driven”, many organizations are eager for integrating ML in there decision making process. Unfortunately, competent data scientists are still relatively scarce, and manual model development cannot keep up with the demand for magic AI solutions. This is no less true when it comes to forecasting. Knowing the future is extremely handy when making decisions. AutoML for Time Series Forecasting Subsequently many systems are now developing and integrating AutoML tools for time series forecasting. https://dasya.itu.dk/for-students/proposals/archive/blockchainbenchmarking/ Tue, 18 Aug 2020 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/blockchainbenchmarking/ Blockchains are often used synonymously with crypto-currencies and unspent transaction output (UTXO) data models, but there are emerging blockchain platforms that offer a more general data model and smart contracts that can manipulate this data freely (e.g. Hyperledger Fabric [1]). As such, these platforms resemble in many ways distributed databases, storing a collection of records, organized as key-value pairs, on the ledger that smarts contracts can access and modify. In several use-cases, permissioned ledgers are being considered as an alternative to traditional databases [2]. https://dasya.itu.dk/for-students/proposals/archive/ppkv/ Tue, 18 Aug 2020 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/ppkv/ Modern data analytics systems are composed of two types of nodes: compute and storage (e.g., Amazon S3, Redis, MongoDB, etc.). The storage nodes typically offer a key-value interface and are often used to store data encoded in a columnar format (e.g., Parquet files). Due to growing data sizes in datacenters, there is an increasing interest in using specialized hardware devices, namely Field Programmable Gate Arrays (FPGAs), to implement the storage nodes in an energy-efficient manner. https://dasya.itu.dk/for-students/proposals/archive/non-intrusive-load-monitoring/ Mon, 06 Jul 2020 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/non-intrusive-load-monitoring/ How much does our smart meter readings disclose about us? Can we disentangle the oven from the washing machine from the kettle? Can we identify demographics and behavior patterns from the stream of electricity data? Most danish homes are now equipped so-called “smart meters” - networked electricity meters that report consumption and load at much higher rate than conventional meters. Currently the meters report data back to a centralized datahub on an hourly basis, but the meters are capable of producing data at a much higher granularity than this.